A smart park energy equipment control method based on an event triggering mechanism

Abstract

This application relates to the field of power supply management technology, and in particular to a smart park energy equipment control method based on an event-triggered mechanism. The method includes: real-time acquisition of photovoltaic active power, energy storage battery state of charge, and load power of load nodes in the smart park; cleaning and processing the acquired data; calculating the net power deficit of the park at the current moment and the instantaneous change in photovoltaic power; calculating the system urgency at any given moment; generating an adaptive dynamic trigger threshold based on the system urgency, wherein the dynamic trigger threshold is inversely proportional to the system urgency; and determining whether to execute event triggering for data transmission and control updates based on the dynamic trigger threshold. The control mechanism in this application can balance communication resource conservation with real-time control.

Description

Technical Field

[0001] This application relates to the field of power supply management technology, and in particular to a smart park energy equipment control method based on an event-triggered mechanism. Background Technology

[0002] Smart park microgrid systems typically integrate distributed photovoltaic systems, energy storage batteries, and various loads. The energy management system needs to collect real-time operating data from each device and issue commands to maintain power balance and power supply stability within the park. Currently, events-triggered or fixed-threshold triggering control mechanisms are mainly used in these projects to achieve data transmission and control.

[0003] Event-triggered mechanisms involve data acquisition and command issuance at fixed time intervals. When photovoltaic output is stable or load fluctuations are small, this method generates a large amount of repetitive and invalid data transmission, severely consuming communication bandwidth. Fixed trigger threshold control mechanisms, on the other hand, trigger communication and control when the change in the controlled variable (such as power deviation) exceeds a preset fixed trigger threshold. This method can reduce invalid data transmission to some extent.

[0004] However, fixed trigger threshold control mechanisms also have some problems. For example, on the one hand, setting the threshold too high leads to system response lag, affecting power quality; setting the threshold too low makes it susceptible to noise interference and triggers frequently, failing to effectively save resources. On the other hand, fixed trigger thresholds ignore the physical state constraints of energy storage batteries (such as state of charge, SOC). This type of algorithm cannot dynamically adjust the trigger threshold according to the battery's safety state. When the battery is in the edge risk region of overcharging or over-discharging, if the system still maintains the original wide threshold, it will not be able to tighten the control dead zone in time to improve control accuracy, which can easily lead to equipment operating beyond limits or even causing safety accidents. Therefore, existing control mechanisms are unable to achieve an effective balance between communication resource conservation and real-time control while ensuring the physical safety of the equipment. Summary of the Invention

[0005] To address the problem of failing to achieve an effective balance between communication resource conservation and real-time control in related technologies, this application provides a smart park energy equipment control method based on an event-triggered mechanism.

[0006] This application provides a smart park energy equipment control method based on an event-triggered mechanism, employing the following technical solution: A smart park energy equipment control method based on an event-triggered mechanism includes: real-time acquisition of photovoltaic active power, energy storage battery state of charge, and load power of load nodes in the smart park; cleaning and processing the acquired data; calculating the net power deficit of the park at the current moment and the instantaneous change in photovoltaic power; calculating the system urgency for any given moment; generating an adaptive dynamic trigger threshold based on the system urgency, wherein the dynamic trigger threshold is inversely proportional to the system urgency; and determining whether to execute event triggering for data transmission and control updates based on the dynamic trigger threshold. The steps for calculating the system urgency include: determining the basic volatility based on the instantaneous change in the current photovoltaic active power, which is positively correlated with the basic volatility; determining the safety risk index based on the current battery state of charge, which characterizes the degree to which the current battery state of charge deviates from the set reference value; and using the product of the basic volatility and the safety risk index as the system urgency.

[0007] The system collects photovoltaic (PV) active power, energy storage battery state of charge (SOC), and load power at load nodes. Based on this data, a system urgency model is constructed, incorporating a base volatility index and a safety risk index. The base volatility reflects fluctuations in PV active power, while the safety risk index reflects the battery state. Furthermore, a dynamic trigger threshold, inversely proportional to the system urgency, is generated. When PV output is stable and battery state is safe, the threshold automatically widens, reducing unnecessary data transmission and control updates. Conversely, when PV power fluctuates drastically or the battery SOC deviates from the safe range, the threshold automatically tightens, ensuring timely communication and control. Compared to traditional technologies that use fixed trigger thresholds, the solution presented in this application achieves a balance between on-demand communication and refined control in smart park energy systems while ensuring the safe operation of energy storage devices.

[0008] Optionally, the difference between the load power of the load nodes in the park and the photovoltaic active power can be used as the net power deficit.

[0009] The load power of the load node in the park represents the electrical load in the power grid, and the photovoltaic active power represents the current output power of the photovoltaic system. The difference between the two reflects the size of the external energy demand that the current power grid system still needs to import.

[0010] Optionally, for any given moment, the difference between the photovoltaic active power at the current moment and the photovoltaic active power at the previous moment can be taken as the instantaneous change in photovoltaic power.

[0011] By using the difference in photovoltaic active power between adjacent moments as the instantaneous change in photovoltaic power, rapid perception of the fluctuation characteristics of photovoltaic output is achieved.

[0012] Optionally, the step of determining the basic volatility based on the instantaneous change in the current photovoltaic active power includes: for any given moment, calculating the ratio of the instantaneous change in the current photovoltaic active power to the rated installed capacity of the photovoltaic system, multiplying the ratio by a preset volatility sensitivity coefficient as the volatility, and using the sum of the volatility and 1 as the basic volatility.

[0013] The ratio of the instantaneous change in photovoltaic active power to the rated installed capacity of the photovoltaic system is calculated to normalize the photovoltaic active power. A fluctuation sensitivity coefficient is introduced to construct a basic fluctuation degree, making power fluctuations under photovoltaic systems of different scales comparable and avoiding unreasonable influences of installed capacity differences on triggering criteria. At the same time, by calculating the sum of 1 and the fluctuation amount, the basic fluctuation degree is ensured to always be greater than or equal to 1, so that the system urgency remains at a baseline level when there is no significant fluctuation, and is linearly amplified when fluctuations intensify, which is conducive to achieving stable and controllable threshold adjustment.

[0014] Optionally, the steps for determining the safety risk index based on the current battery state of charge include: for any given moment, taking the absolute value of the difference between the current battery state of charge and the ideal state of charge reference value as the deviation, and performing an exponential operation on the product of the deviation and the state constraint penalty coefficient to obtain the safety risk index.

[0015] By incorporating the deviation of the battery's state of charge from an ideal reference value into an exponential function to construct a safety risk index, the risk impact of energy storage batteries is nonlinearly amplified when they approach overcharge or over-discharge boundaries. This enhances the system's sensitivity to battery safety boundaries, ensuring that even with small power fluctuations, the system's urgency rapidly increases as long as the battery state approaches the danger zone, prompting the control strategy to tighten thresholds promptly.

[0016] Optionally, the step of generating an adaptive dynamic trigger threshold based on system urgency includes: for any given time, calculating the ratio of a preset basic error limit to the system urgency, and using the sum of this ratio and a preset minimum protection threshold as the dynamic trigger threshold.

[0017] When the system urgency increases, the ratio of the preset basic error limit to the system urgency decreases and gradually approaches 0, thus reducing the final dynamic trigger threshold and approaching the preset minimum protection threshold. Conversely, when the system urgency decreases, the ratio of the preset basic error limit to the system urgency increases or decreases, leading to an increase in the dynamic trigger threshold, thereby achieving adaptive adjustment of the dynamic trigger threshold.

[0018] Optionally, the step of determining whether to execute event triggering for data transmission and control updates based on a dynamic triggering threshold includes: for any given time, calculating the difference between the net power deficit corresponding to that time and the net power deficit data transmitted when the event was successfully triggered last time; in response to the difference being greater than the dynamic triggering threshold corresponding to that time, determining whether to execute event triggering and performing data transmission and control updates.

[0019] By comparing the current net power deficit with the net power deficit at the time of the last successful trigger, the event triggering criteria are given a memory, effectively filtering out short-term swings or minor oscillations. Combined with a dynamic triggering threshold, communication and control updates are only triggered when there is a substantial change in power status. This significantly reduces redundant data transmission while ensuring control effectiveness, improving the robustness of the event triggering mechanism in real-world operating environments.

[0020] Optionally, the data transmission may include: packaging the current net power deficit and battery state of charge, and uploading them to the energy management system within the park via the TCP / IP protocol stack.

[0021] Optionally, the steps for collecting the photovoltaic active power, the state of charge of the energy storage battery, and the load power of the load node in the smart park include: acquiring the raw data collected by the sensor, and performing a moving average filtering algorithm on the raw data to obtain the photovoltaic active power, the state of charge of the energy storage battery, and the load power of the load node in the smart park.

[0022] A moving average filtering algorithm is introduced during the data acquisition stage to smooth the raw sensor data, effectively suppressing the impact of on-site electromagnetic interference and random noise on the power calculation results.

[0023] Optionally, the photovoltaic active power, the state of charge of the energy storage battery, and the load power of the load node are obtained from the photovoltaic inverter and the battery management system by the local controller deployed in the smart park via the communication bus.

[0024] This application has the following technical effects: By constructing a nonlinear system urgency model that integrates photovoltaic power fluctuation sensing and energy storage battery state of charge constraints, dynamic adjustment of the trigger threshold is achieved. This solves the problem that the traditional fixed trigger threshold mechanism cannot balance communication resource conservation and equipment physical safety. It enables the energy management system to significantly reduce redundant transmission to save bandwidth during the photovoltaic stable period, and automatically tighten the threshold to ensure control real-time performance and safety when fluctuations are severe or the battery is in an edge state. Attached Figure Description

[0025] Figure 1 This is a flowchart of a smart park energy equipment control method based on an event-triggered mechanism, according to an embodiment of this application.

[0026] Figure 2 This is a graph showing the change in photovoltaic active power in the embodiments of this application.

[0027] Figure 3 This is the response diagram of a traditional fixed trigger threshold control mechanism.

[0028] Figure 4 This is a response diagram of the dynamic trigger threshold control mechanism in the embodiments of this application. Detailed Implementation

[0029] This application discloses a smart park energy equipment control method based on an event-triggered mechanism. By collecting operational data from photovoltaic, energy storage, and load systems, a system urgency model incorporating volatility perception and state constraints is constructed. Based on this model, an adaptive dynamic trigger threshold is generated. Ultimately, the threshold is automatically relaxed during periods of stable photovoltaic activity to significantly reduce communication redundancy, and automatically tightened when fluctuations are severe or battery power is at its limit, thus resolving the contradiction between communication resource conservation and real-time control.

[0030] Reference Figure 1 A smart park energy equipment control method based on an event-triggered mechanism includes steps S1-S4.

[0031] S1: Real-time collection of photovoltaic active power, energy storage battery state of charge, and load power of load nodes in the smart park, and cleaning and processing of the collected data to calculate the net power deficit of the park at the current moment and the instantaneous change in photovoltaic power.

[0032] In this embodiment, the local controller of the smart park (exemplarily an embedded edge computing gateway based on an ARM Cortex-M7 core) communicates with the underlying devices via an RS485 bus or CAN bus interface. The controller uses... The system uses a base sampling frequency to read in real time the active power output of the photovoltaic inverter, the real-time state of charge uploaded by the battery management system (BMS), and the real-time power of the total load nodes in the park.

[0033] To eliminate high-frequency noise introduced by the electromagnetic environment, the controller preprocesses the acquired raw data. Specifically, the controller is configured with a length... The first-in-first-out (FIFO) queue is used as a sliding window, and a moving average filtering algorithm is employed to smooth the data within the window, thereby obtaining the current time. Real-time observations.

[0034] Based on the smoothed data, calculate the net power deficit of the park's microgrid at the current moment. In addition, the instantaneous change in photovoltaic power reflects the amount of regulation required to maintain the power balance of the microgrid, and the instantaneous change in photovoltaic power reflects the degree of fluctuation in active power.

[0035] The formula for calculating the net power deficit can be expressed as follows: In the formula, Indicates time The net power deficit of the park's microgrid; This represents the photovoltaic active power at the current sampling time; Indicates time The active power of the total load nodes in the park.

[0036] The formula for calculating the instantaneous change in photovoltaic power can be expressed as: In the formula, Indicates time The instantaneous change in photovoltaic power; Indicates time Photovoltaic active power; This represents the photovoltaic active power at the previous sampling time. If this difference increases, it indicates a sudden change in photovoltaic output, and the system needs to be more vigilant to cope with possible power surges.

[0037] Assuming the rated installed capacity of the photovoltaic system The photovoltaic power collected at the previous sampling time was... The photovoltaic power collected at the current moment is The instantaneous change in photovoltaic power If the total load at this time Then the net power deficit .

[0038] S2: Calculate the urgency of the system at any given time.

[0039] To address the issue that traditional fixed trigger thresholds cannot detect device physical constraints, this step establishes system urgency. The steps for calculating the system urgency include: determining the basic volatility based on the instantaneous change in the current photovoltaic active power, which is positively correlated with the basic volatility; determining the safety risk index based on the current battery state of charge, which characterizes the degree to which the current battery state of charge deviates from the set reference value; and multiplying the basic volatility by the safety risk index as the system urgency.

[0040] Specifically, the formula for calculating the urgency of the system is expressed as follows: ; In the formula, Indicates time The urgency of the system; The fluctuation sensitivity coefficient is preferably set to [value] in this embodiment. ; Indicates time The instantaneous change in photovoltaic power; The rated installed capacity of the photovoltaic system is taken as a value. ; The state constraint penalty coefficient is set to [value] based on experience in this embodiment. ; The current state of battery charge, with a value range of [value range missing]. ; The ideal state of charge (SOC) reference value for energy storage batteries is typically set as follows: (Right now Battery level); Indicated by An exponential function with base 0.

[0041] In the formula, Indicates the fluctuation amount. Based on the volatility, the instantaneous change in photovoltaic power. The linear increase in this value reflects the direct response to source disturbances.

[0042] This is a safety risk index, reflecting the degree to which the current state of charge of the battery deviates from the ideal range. The higher the value, the more likely the battery is to be overcharged or over-discharged, and the more dangerous the current battery condition. The value increases exponentially. The product of these two factors means that when the battery is in a critical state, the urgency level increases even if the photovoltaic fluctuations are small.

[0043] For example, suppose the system parameters are set as follows: Instantaneous change in current photovoltaic power Battery state of charge .

[0044] After substituting into the formula, the basic volatility is: The safety risk index is: The final calculated system urgency is: At this point, the urgency level is relatively low, close to the baseline value of 1.

[0045] S3: Generate an adaptive dynamic trigger threshold based on the system urgency, wherein the dynamic trigger threshold is inversely proportional to the system urgency.

[0046] To enable on-demand communication, the controller constructs a dynamic trigger threshold that is allowed at the current moment based on the calculated system urgency. .

[0047] Specifically, the formula for calculating the dynamic trigger threshold is expressed as follows: In the formula, Indicates time Dynamic trigger threshold; The preset basic error limit is set to [value] in this embodiment. , representing the maximum control dead zone allowed under ideal system conditions; The preset minimum protection threshold is set to [value]. ; For a moment The urgency of the system.

[0048] A high system urgency indicates significant fluctuations in the active power output of the photovoltaic system, coupled with poor battery condition. In such cases, as the system urgency increases, the denominator grows, leading to… The threshold is rapidly reduced, bringing the dynamic triggering threshold close to the minimum protection threshold, thus improving sensitivity to minute power changes and preventing the loss of critical control information due to excessively high thresholds under dangerous operating conditions. Conversely, when the system urgency is low, it indicates that the active power output of the photovoltaic system is stable and the battery is in a safe state. The increase in some parameters, which in turn dynamically triggers an increase in the threshold, can filter out minor fluctuations, significantly reduce communication redundancy, and save communication resources.

[0049] This is mainly to prevent Zeno Behavior caused by the threshold approaching zero, thus ensuring the physical upper limit of the communication frequency.

[0050] For example, if the instantaneous change in photovoltaic power Meanwhile, the current state of battery charge Substitute the urgency of the computing system At this point, the dynamic trigger threshold is... The threshold is relatively wide, capable of filtering out values ​​smaller than [a certain threshold]. Noise fluctuations.

[0051] Similarly, if the instantaneous change in photovoltaic power increases to ,at the same time, Nearly fully charged, assuming a high urgency in computing system performance. At this point, the dynamic trigger threshold is... The threshold tightens rapidly, and the system detects values ​​exceeding [a certain threshold]. It responds immediately to any deviation to prevent overcharging and ensure battery safety.

[0052] S4: Determine whether to trigger an event based on a dynamic trigger threshold for data transmission and control updates.

[0053] The controller's memory stores the last successful trigger moment. Net power deficit data transmitted In each sampling period, the controller executes the following decision logic: In the formula, time The net power deficit of the park's microgrid; Last successfully triggered moment The net power deficit data transmitted; Indicates time The dynamic trigger threshold.

[0054] In the formula, This represents the absolute value of the current net power deficit relative to the previous transmission value; This is the adaptive dynamic trigger threshold calculated at the current moment.

[0055] At any moment The difference between the net power deficit of the park's microgrid and the net power deficit transmitted at the time of the last successful triggering event is greater than the value at time [time missing]. When the corresponding dynamic trigger threshold is reached, it indicates that the current power deviation has exceeded the error range allowed by the current system urgency. At this time, the local controller immediately wakes up the communication module and sends the current net power deficit. The battery state of charge is packaged and uploaded to the park's energy management system (EMS) via the TCP / IP protocol stack. Simultaneously, the controller updates the anchor point data in memory, recording the last successful trigger time. Updated to the current time and historical data Update to current value .

[0056] If the above inequality does not hold, it means that the current power deviation is still within the allowable dead zone, and the system believes that the current state does not require updating control commands. At this time, the controller does not send data, and the communication module remains in sleep mode, thereby reducing communication bandwidth usage and power consumption of edge devices.

[0057] Combination Figure 2 , Figure 2 This graph shows the variation in photovoltaic active power, reflecting the actual change in photovoltaic active power. (Reference) Figure 3 and Figure 4 , Figure 3 The response graph of the fixed trigger threshold control mechanism has very densely distributed trigger points on the time axis, covering almost the entire time period. This causes the actual trigger events to be affected by background noise, resulting in a large amount of invalid and redundant communication. Figure 4The response graph of the dynamic trigger threshold control mechanism in this application shows strong sparsity and specificity, providing necessary security monitoring.

[0058] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A smart park energy equipment control method based on an event-triggered mechanism, characterized in that, The system collects real-time data on the active power of photovoltaics in the smart park, the state of charge of energy storage batteries, and the load power of load nodes. It also cleans and processes the collected data to calculate the net power deficit of the park at the current moment and the instantaneous change in photovoltaic power. Calculate the system urgency at any given moment; An adaptive dynamic trigger threshold is generated based on the system urgency, and the dynamic trigger threshold is inversely proportional to the system urgency. Whether to trigger an event for data transmission and control updates is determined based on a dynamic trigger threshold; The steps for calculating the system urgency include: determining the basic volatility based on the instantaneous change in the current photovoltaic active power, which is positively correlated with the basic volatility; determining the safety risk index based on the current battery state of charge, which characterizes the degree to which the current battery state of charge deviates from the set reference value; and using the product of the basic volatility and the safety risk index as the system urgency.

2. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The difference between the load power of the load nodes in the park and the photovoltaic active power is taken as the net power deficit.

3. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, For any given moment, the difference between the photovoltaic active power at the current moment and the photovoltaic active power at the previous moment is taken as the instantaneous change in photovoltaic power.

4. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The steps for determining the basic volatility based on the instantaneous change in the current photovoltaic active power include: for any given moment, calculating the ratio of the instantaneous change in the photovoltaic active power to the rated installed capacity of the photovoltaic system, multiplying the ratio by a preset volatility sensitivity coefficient as the volatility, and using the sum of the volatility and 1 as the basic volatility.

5. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The steps for determining the safety risk index based on the current battery state of charge include: for any given moment, taking the absolute value of the difference between the current battery state of charge and the ideal state of charge reference value as the deviation, and performing an exponential operation on the product of the deviation and the state constraint penalty coefficient to obtain the safety risk index.

6. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The steps for generating an adaptive dynamic trigger threshold based on system urgency include: for any given time, calculating the ratio of a preset basic error limit to the system urgency, and using the sum of this ratio and a preset minimum protection threshold as the dynamic trigger threshold.

7. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The steps for determining whether to trigger an event for data transmission and control updates based on a dynamic trigger threshold include: for any given moment, calculating the difference between the net power deficit at that moment and the net power deficit data transmitted when the event was successfully triggered last time; and in response to the difference being greater than the dynamic trigger threshold at that moment, determining whether to trigger the event and performing data transmission and control updates.

8. The smart park energy equipment control method based on an event-triggered mechanism according to claim 7, characterized in that, The data transmission includes: packaging the current net power deficit and battery state of charge, and uploading them to the energy management system within the park via the TCP / IP protocol stack.

9. The smart park energy equipment control method based on an event-triggered mechanism according to claim 1, characterized in that, The steps for collecting photovoltaic active power, energy storage battery state of charge, and load power of load nodes in a smart park include: acquiring raw data collected by sensors, and applying a moving average filtering algorithm to the raw data to obtain the photovoltaic active power, energy storage battery state of charge, and load power of load nodes in the smart park.

10. A smart park energy equipment control method based on an event-triggered mechanism according to claim 9, characterized in that, The photovoltaic active power, the state of charge of the energy storage battery, and the load power of the load node are obtained from the photovoltaic inverter and the battery management system by the local controller deployed in the smart park through the communication bus.

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